Deep Learning at Scale

Part III. Extreme Scaling

This section delves into the aspects of scaling deep learning models to effectively handle vast amounts of data. It starts by emphasizing a data-centric approach, offering techniques to maximize data utilization and optimize data pipelines through sampling and selection methods. This section also focuses on scaling experiments, providing insights into effective experiment planning and management for improved model performance. Additionally, it explores efficient fine-tuning of large models using low-rank techniques and introduces the conceptual framework of foundation models, summarizing their significance in the evolving deep learning landscape.